{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# XGBoost Regressor Part 2"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo_finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:07.697Z",
          "iopub.execute_input": "2021-06-13T05:15:07.704Z",
          "iopub.status.idle": "2021-06-13T05:15:09.213Z",
          "shell.execute_reply": "2021-06-13T05:15:09.233Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.220Z",
          "iopub.execute_input": "2021-06-13T05:15:09.226Z",
          "iopub.status.idle": "2021-06-13T05:15:09.567Z",
          "shell.execute_reply": "2021-06-13T05:15:09.606Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create more data\n",
        "dataset['Increase/Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,-1)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,-1)\n",
        "dataset['Return'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Increase/Decrease  \\\nDate                                                                          \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n\n            Buy_Sell_on_Open  Buy_Sell    Return  \nDate                                              \n2014-01-03                 1         1  0.012658  \n2014-01-06                 1         1  0.032500  \n2014-01-07                 1        -1  0.012106  \n2014-01-08                -1        -1  0.000000  \n2014-01-09                -1         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase/Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Return</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.576Z",
          "iopub.execute_input": "2021-06-13T05:15:09.582Z",
          "iopub.status.idle": "2021-06-13T05:15:09.622Z",
          "shell.execute_reply": "2021-06-13T05:15:09.610Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "(1257, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.630Z",
          "iopub.execute_input": "2021-06-13T05:15:09.636Z",
          "iopub.status.idle": "2021-06-13T05:15:09.647Z",
          "shell.execute_reply": "2021-06-13T05:15:09.613Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset.iloc[33:]\n",
        "df = dataset[:-1]      \n",
        "\n",
        "df.index = range(len(df))"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.657Z",
          "iopub.execute_input": "2021-06-13T05:15:09.661Z",
          "iopub.status.idle": "2021-06-13T05:15:09.669Z",
          "shell.execute_reply": "2021-06-13T05:15:09.695Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_size  = 0.20\n",
        "valid_size = 0.20\n",
        "\n",
        "test_split_idx  = int(df.shape[0] * (1-test_size))\n",
        "valid_split_idx = int(df.shape[0] * (1-(valid_size+test_size)))\n",
        "\n",
        "train_df  = df.loc[:valid_split_idx].copy()\n",
        "valid_df  = df.loc[valid_split_idx+1:test_split_idx].copy()\n",
        "test_df   = df.loc[test_split_idx+1:].copy()\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.scatter(train_df.index, train_df['Adj Close'], label='Training')\n",
        "plt.scatter(valid_df.index, valid_df['Adj Close'], label='Validation')\n",
        "plt.scatter(test_df.index,  test_df['Adj Close'],  label='Test')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1152x576 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.677Z",
          "iopub.execute_input": "2021-06-13T05:15:09.682Z",
          "iopub.status.idle": "2021-06-13T05:15:09.798Z",
          "shell.execute_reply": "2021-06-13T05:15:09.880Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drop_cols = ['Close', 'Increase/Decrease', 'Buy_Sell_on_Open', 'Buy_Sell']\n",
        "\n",
        "dataset = dataset.dropna()\n",
        "train_df = train_df.drop(drop_cols, 1)\n",
        "valid_df = valid_df.drop(drop_cols, 1)\n",
        "test_df  = test_df.drop(drop_cols, 1)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.806Z",
          "iopub.execute_input": "2021-06-13T05:15:09.811Z",
          "iopub.status.idle": "2021-06-13T05:15:09.822Z",
          "shell.execute_reply": "2021-06-13T05:15:09.886Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = train_df['Adj Close'].copy()\n",
        "X_train = train_df.drop(['Adj Close'], 1)\n",
        "\n",
        "y_valid = valid_df['Adj Close'].copy()\n",
        "X_valid = valid_df.drop(['Adj Close'], 1)\n",
        "\n",
        "y_test  = test_df['Adj Close'].copy()\n",
        "X_test  = test_df.drop(['Adj Close'], 1)\n",
        "\n",
        "X_train.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 754 entries, 0 to 753\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Open    754 non-null    float64\n",
            " 1   High    754 non-null    float64\n",
            " 2   Low     754 non-null    float64\n",
            " 3   Volume  754 non-null    int64  \n",
            " 4   Return  754 non-null    float64\n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 29.6 KB\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.831Z",
          "iopub.execute_input": "2021-06-13T05:15:09.838Z",
          "iopub.status.idle": "2021-06-13T05:15:09.848Z",
          "shell.execute_reply": "2021-06-13T05:15:09.891Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "parameters = {\n",
        "    'n_estimators': [100, 200, 300, 400],\n",
        "    'learning_rate': [0.001, 0.005, 0.01, 0.05],\n",
        "    'max_depth': [8, 10, 12, 15],\n",
        "    'gamma': [0.001, 0.005, 0.01, 0.02],\n",
        "    'random_state': [42]\n",
        "}\n",
        "\n",
        "eval_set = [(X_train, y_train), (X_valid, y_valid)]\n",
        "model = xgb.XGBRegressor(max_depth=5, learning_rate=0.01, n_estimators=2000, colsample_bytree=0.1)\n",
        "clf = GridSearchCV(model, parameters)\n",
        "\n",
        "clf.fit(X_train, y_train, verbose=False)\n",
        "\n",
        "print(f'Best params: {clf.best_params_}')\n",
        "print(f'Best validation score = {clf.best_score_}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best params: {'gamma': 0.02, 'learning_rate': 0.05, 'max_depth': 8, 'n_estimators': 400, 'random_state': 42}\n",
            "Best validation score = -0.022163284567563356\n",
            "Wall time: 1min 33s\n"
          ]
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:15:09.858Z",
          "iopub.execute_input": "2021-06-13T05:15:09.863Z",
          "iopub.status.idle": "2021-06-13T05:16:43.837Z",
          "shell.execute_reply": "2021-06-13T05:16:43.873Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "model = xgb.XGBRegressor(**clf.best_params_, objective='reg:squarederror')\n",
        "model.fit(X_train, y_train, eval_set=eval_set, verbose=False)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wall time: 192 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0.02, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.05, max_delta_step=0, max_depth=8,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=400, n_jobs=0, num_parallel_tree=1, random_state=42,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:16:43.850Z",
          "iopub.execute_input": "2021-06-13T05:16:43.858Z",
          "iopub.status.idle": "2021-06-13T05:16:44.055Z",
          "shell.execute_reply": "2021-06-13T05:16:44.087Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance\n",
        "\n",
        "plot_importance(model)\n",
        "\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:16:44.066Z",
          "iopub.execute_input": "2021-06-13T05:16:44.074Z",
          "iopub.status.idle": "2021-06-13T05:16:44.197Z",
          "shell.execute_reply": "2021-06-13T05:16:44.187Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "print('y_true = %s' % np.array(y_test)[:5])\n",
        "print('y_pred = %s' % y_pred[:5])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_true = [10.27999973 10.97999954 11.55000019 12.11999989 11.88000011]\n",
            "y_pred = [10.486605 10.667755 11.433056 11.774805 11.841607]\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:16:44.209Z",
          "iopub.execute_input": "2021-06-13T05:16:44.215Z",
          "iopub.status.idle": "2021-06-13T05:16:44.231Z",
          "shell.execute_reply": "2021-06-13T05:16:44.301Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('mean_squared_error = %s' % mean_squared_error(y_test, y_pred))\n",
        "\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean_squared_error = 67.1401108921124\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-06-13T05:16:44.242Z",
          "iopub.execute_input": "2021-06-13T05:16:44.249Z",
          "iopub.status.idle": "2021-06-13T05:16:44.265Z",
          "shell.execute_reply": "2021-06-13T05:16:44.305Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(2)\n",
        "\n",
        "fig.set_size_inches(16, 10)\n",
        "\n",
        "predicted_prices = df.loc[test_split_idx+1:].copy()\n",
        "predicted_prices['Adj Close'] = y_pred\n",
        "\n",
        "axs[0].scatter(df.index, y=df['Adj Close'],\n",
        "                         label='Truth')\n",
        "\n",
        "axs[0].scatter(predicted_prices.index, predicted_prices['Adj Close'],\n",
        "                         label='Prediction')\n",
        "\n",
        "axs[1].scatter(predicted_prices.index, y_test, label='Truth')\n",
        "\n",
        "axs[1].scatter(predicted_prices.index, y_pred,label='Prediction')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1152x720 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-13T05:16:44.279Z",
          "iopub.execute_input": "2021-06-13T05:16:44.288Z",
          "iopub.status.idle": "2021-06-13T05:16:44.421Z",
          "shell.execute_reply": "2021-06-13T05:16:44.432Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}